A Use Case of Packets' Significance Difference with Media Scalability
draft-dong-usecase-packet-significance-diff-00
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draft-dong-usecase-packet-significance-diff-00
Independent Submission L. Dong
Internet-Draft K. Makhijani
Intended status: Informational R. Li
Expires: December 18, 2021 Futurewei Technologies Inc.
June 16, 2021
A Use Case of Packets' Significance Difference with Media Scalability
draft-dong-usecase-packet-significance-diff-00
Abstract
This document introduces a use case of packets' significance
difference embedded with media scalability. With the dominance of
video traffic on the Internet, selectively dropping packets or parts
of packets from competing media streams becomes a complementary
mechanism when dealing with network congestion.
The document describes the characteristics of media scalability, some
limitations of existing end-to-end congestion control mechanisms
through rate control and adaptation, explains why current ways of
entire packet dropping at the traffic class level using in-network
active queue management are not most appropriate to meet end users'
Quality of Service expectations. The document identifies that there
exists "significance difference" among packets or even among parts of
the packets within a flow, and brings out a new set of requirements
for application and network to support packet significance difference
to improve the Quality of Experience of end users.
Status of This Memo
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This Internet-Draft will expire on December 18, 2021.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terms and Abbreviations . . . . . . . . . . . . . . . . . . . 3
3. Media Scalability and Congestion Control . . . . . . . . . . 4
4. Packet Dropping . . . . . . . . . . . . . . . . . . . . . . . 5
5. Significance Difference Among Packets and Within Packets . . 6
6. New Requirements . . . . . . . . . . . . . . . . . . . . . . 7
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
8. Security Considerations . . . . . . . . . . . . . . . . . . . 8
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 8
10. Informative References . . . . . . . . . . . . . . . . . . . 8
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
Recent studies [CiscoNetworkingIndex] show that IP video traffic will
be 82 percent of all consumer Internet traffic by 2021 in a global
scale, up from 73 percent in 2016. Live video has grown 15-fold from
2016 to 2021, accounts for 13 percent of Internet video traffic by
2021. VR (Virtual Reality) and AR (Augmented Reality) traffic has
increased 20-fold between 2016 and 2021, at a CAGR (Compound Annual
Growth Rate) of 82 percent. With the rapid growth of multimedia
streaming traffic, it is increasingly likely that multiple streaming
flows share a bottleneck link, which would inevitably cause network
congestion. Today's transport protocols and Internet protocols are
oblivious to multimedia streaming applications or end users' QoE
(Quality of Experience) expectations. From the perspective of user
experience and user expectation, the following two observations could
be made.
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o It is very likely that a user may prefer to acquire the media
content in a somewhat degraded quality that is above the tolerance
threshold rather than getting nothing at all for a few seconds.
o A user may be particularly interested in certain group of blocks
belonging to the interested objects in the media content (i.e.,
Region of Interest, RoI). It is necessary to prevent the RoI
blocks from being lost during transmission.
At the beginning of this document, the different types of scalability
are discussed in current video codecs, facilitating the rate control
and adaptation mechanisms carried out in video segments when dealing
with network congestion during the media streaming. It is
acknowledged that such mechanisms have efficiently improved users'
QoE. However, the packets on the wire cannot avoid the possibility
of being entirely dropped when the bottleneck network nodes cannot
retain them due to buffer overflowing during congestion. Thanks to
the scalability characteristics designed to the video codecs, it is
not hard to find out that the importance or significance of different
packets within a media streaming flow or even different parts of the
single packet could vary for their usefulness in decoding and
recovering the media content to meet receiver's expectation. The
document highlights the requirements of making the user' preference
and application context aware to the network to help further improve
the QoE of media streaming. Accordingly, the network could treat the
packets or different parts of the packets according to the
characteristics of the packets and end users' preferences.
2. Terms and Abbreviations
The terms and abbreviations used in this document are listed below.
o AR: Augmented Reality
o CAGR: Compound Annual Growth Rate
o DASH: Dynamic Adaptive Streaming over HTTP
o GOP: Group of Picture
o HAS: HTTP Adaptive Stream
o HTTP: Hypertext Transfer Protocol
o QoE: Quality of Experience
o QoS: Quality of Service
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o SNR: Signal-to-Noise Ratio
o SVC: Scalable Video Coding
o VR: Virtual Reality
The above terminology is defined in greater details in the remainder
of this document.
3. Media Scalability and Congestion Control
A visual scene is represented in digital form by sampling the real
scene spatially on a rectangular grid in the video image plane and
sampling temporally at regular time intervals as a sequence of still
frames. Correspondingly, modern media codec [Conklin2001] [Kim2001]
incorporates three types of "Scalability": i.e., temporal
scalability, spatial scalability, and quality scalability, which
adapt the media bitstream by adding or removing some portions to/from
it in order to match the different needs or preferences of end users
as well as to the network conditions.
Temporal scalability refers to scalability designed to allow the
frame rate of the video bitstream to be varied using interlayer
prediction. Spatial scalability represents the spatial resolution
variations with respect to the original image frame. The lower layer
provides the basic spatial resolution. The enhancement layer employs
the spatially interpolated lower layers and constructs the source
video in its full spatial resolution. Quality scalability is also
commonly referred to as fidelity or SNR (Signal-to-Noise Ratio)
scalability. Each spatial layer could have many quality layers. For
example, SVC (Scalable Video Coding)[SVC] is an H.264 [H.264]
extension that divides a single video bitstream into multiple
representations or layers. This hierarchical layered structure
comprises a base layer and two enhancement layers. The media may be
scaled up by adding the enhancement layer(s) or scaled down by
dropping the enhancement layer(s). The levels of scalability
included in the media stream affect the quality of media presented to
the end users' devices.
Bursty loss and longer-than-expected delay have catastrophic effect
on QoE to end-users in media streaming. They are usually caused by
network congestion. Despite all kinds of congestion control
mechanisms developed in the community over the decades [Saadi2019]
[Adams2013], they often target different goals, e.g., link
utilization improvement, loss reduction, fairness enhancement. By
leveraging the flexibility and variety of media qualities provided by
different types of media scalability, for media streaming, minimizing
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the possibility of network congestion can often be achieved by rate
control and media adaptation methods.
Existing rate control and adaptation methods [Bentaleb2019] [Wu2001]
can be at source-side and receiver-side, which are carried at end
devices and servers, respectively.
o In source-based schemes [Wu2000] , source regulates the sending
rate to maintain the packet loss ratio below a threshold by
employing the feedback from probing experiments, or source
determines the sending rate through a TCP-friendly model.
However, some constraints exist, media codecs can usually only
adjust their output rates in a much more coarse-grained fashion
than, for example, TCP. Users' QoE would also suffer if encoding
rates are switched too frequently.
o HTTP (Hypertext Transfer Protocol)-based dynamic video adaptation
methods [Kua2017] could be driven by source. The server collects
the feedback from the network and client (e.g., dynamic variation
of network bandwidth and receiving buffer capacity of the client),
and accordingly, the video quality will be adapted and streamed.
On the other hand, adaptation techniques are also proposed at
receiver-side, which mainly use DASH (Dynamic Adaptive Streaming
over HTTP) [MPEG-DASH-SAND] [MPEG-DASH] and HAS (HTTP Adaptive
Stream) for streaming adapted video data.
o The receiver-based rate control [McCanne1996] is typically used in
multicasting scalable media content, which is split into multiple
layers, with each layer corresponding to one channel in the
multicast tree. Receivers could regulate their own receiving
rates by adding/dropping channels. Thus receiver-based rate has
its limited usage in unicasting. All these techniques consider
full quality while streaming from sender to receivers; hence, they
consume more resources in the network.
4. Packet Dropping
Acknowledging the benefits offered by various congestion control and
congestion avoidance mechanisms, we would like to point out that the
feedback and rate adaption might not be prompt enough to cope with
the dropping of packets on the wire.
In the current Internet, a packet is treated as the minimal,
independent, and self-sufficient unit that gets classified,
forwarded, or dropped completely by a network node, according to the
local configuration and congestion condition. Although congestion
discard can be mitigated by a mixture of ingress traffic shaping and
active queue management mechanisms [Thiruchelvi2008] [Adams2013] to
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avoid any network resource overdrawn, it is not feasible to be
deployed on a large scale, meanwhile wastes network resources
preparing for the worst possible scenario.
DiffServ [RFC2475] is is used to manage resources such as bandwidth
and queuing buffers on a per-hop basis between different classes of
traffic. The Internet traffic may be separated into different
classes with differentiated priorities. This allows preferential
treatment for latency or loss sensitive traffic over more tolerant
applications, for example those that can afford retransmission.
However, with video traffic dominating Internet traffic, flows of
media streaming applications with the same class still compete for
network resources when encountering bottleneck links and fighting
network congestion, preference decided on traffic class would not be
effective to eliminate the possibility of degraded service levels or
packet drops due to collisions with each other.
The routers treat every bit/byte in the packet payload equally, which
means every bit/byte has the same significance to the routers. Each
to-be-dropped packet is discarded completely. If the transport layer
protocol is TCP, after timeout or duplicate acknowledgements received
at the sender, the sender may re-try to send the dropped packet
before the maximum number of re-transmissions reaches.
Retransmission of packets wastes network resources, reduces the
overall throughput of the connection and causes longer latency for
the packet delivery. The study [RFC8836] has shown that a loss rate
of 1% is tolerable to users while a loss rate of 3% is intolerable to
most users who found the quality to be annoying (or worse), according
to the subjective opinions of the effects of packet loss on media
quality. Therefore, the current way of handling network congestion
by discarding the packet entirely and retransmitting the packets in a
blind-of-application-context manner is not very suitable for media
streaming.
5. Significance Difference Among Packets and Within Packets
With the various scalability implemented in the media codec, some
bits of an encoded media stream are more important than others. Bits
belonging to base layer usually are more significant to the decoder
than bits belonging to enhancement layers. For example, I-frames
hold complete picture data [Orosz2015] and is frequently referenced
by the subsequent frames. It is inserted by the encoder when the
scene changes. Losing the first I-frame in the GOP (Group of
Pictures) would cause video picture even missing for few seconds,
because P- and B-frames referencing to the I-frame would not be
decoded nor displayed either. Thus, I-frames are most essential in
the media stream, which have the most effect on perceived video
quality, and such effect can last through the whole GOP. P- and
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B-frames are inserted at appropriate places to reduce the video size
or bitrate and are tuned to maintain a certain video quality level.
P-frame stands for Predicted Frame and allows macroblocks to be
compressed using temporal prediction in addition to spatial
prediction. Video scenes with a low level of movement are less
sensitive to both B-frame and P-frame packet loss, alternatively
video scenes with a high level of movement are more sensitive to both
B-frame and P-frame packet loss. A lost P-frame can impact the
remaining part of the GOP. A lost B-frame has only local effects in
a slowly moving content or with large static background. In a scene
of a dynamically moving content, losing B-frame has more dramatic
impact and its scale can be as far-reaching as a P-frame loss.
As another example, macroblocks that are identified to represent the
objects in RoI are likely more important than other macroblocks of
non-RoI regions. For packets carrying RoI macroblocks in the media
stream need to have higher priority to be retained compared to other
packets carrying non-RoI macroblocks.
On the other hand, let's say that the end-users can reveal their
preferences to the network, e.g., degree of tolerance to the decoded
media content' quality degradation, which might reflect visually such
as resolution reduction, missing objects in non-RoI regions, the
network could selectively drop packets in a differentiated manner
according to such information. This avoids retransmission or delay
of those packets with higher significance, reduce the experienced
end-to-end latency of end users, and maintain the continuous
streaming of the media. This is achieved at the cost of dropping
lower-significance packets.
6. New Requirements
We have discussed in the previous sections that due to the various
types of scalability implemented in the media codecs, "significance
difference" exists among packets or even among parts of the packets.
In other words, some packets containing the more important
macroblocks (e.g., RoI macroblocks, base layer macroblocks) show
higher significance than other packets for the media decoding at the
receiver side and the improvement of QoE of end users. In order for
the network be able to treat the packets of media streams in a
differentiated manner and at finer granularity than DiffServ, the
application shall reveal some information to the network to enable
selective packet dropping or partial packet dropping. Some examples
are listed below:
o Receiving end user's preference on media quality, e.g. tolerable
quality degradation regarding for example resolution.
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o Labeling of the packets or some parts of the packets that
correspond to receiver's interested objects as RoI.
o Characteristics of media content contained in the packets, e.g.
frame type, movement level.
Correspondingly, the network shall be able to leverage the above
information revealed by the application, and selectively drop packets
or parts of the packets from competing media streaming flows with
precedence order when network congestion happens. The retransmission
could be maximumly eliminated. The receiving end user is able to
consume the delivered packets as many as possible in-time with
acceptable quality.
7. IANA Considerations
This document requires no actions from IANA.
8. Security Considerations
This document introduces no new security issues.
9. Acknowledgements
10. Informative References
[Adams2013]
Adams, R., "Active Queue Management: A Survey", IEEE
Communications Surveys and Tutorials, vol. 15, no. 3, pp.
1425-1476, 2013, <https://ieeexplore.ieee.org/stamp/
stamp.jsp?arnumber=6329367>.
[Bentaleb2019]
Bentaleb, A., Taani, B., Begen, A. C., Timmerer, C., and
R. Zimmermann, "A Survey on Bitrate Adaptation Schemes for
Streaming Media Over HTTP", IEEE Communications Surveys
and Tutorials, vol. 21, no. 1, pp. 562-585, 2019,
<https://ieeexplore.ieee.org/document/8424813>.
[CiscoNetworkingIndex]
Cisco, "Cisco Visual Networking Index: Forecast and
Methodology, 2016 to 2021", June 2017,
<https://www.cisco.com/c/en/us/solutions/collateral/
executive-perspectives/annual-internet-report/white-paper-
c11-741490.html>.
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[Conklin2001]
Conklin, G. J., Greenbaum, G. S., Lillevold, K. O.,
Lippman, A. F., and Y. A. Reznik, "Video Coding for
Streaming Media Delivery on the Internet", IEEE
Transactions on Circuits and Systems for Video
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[H.264] ITU-T, "H.264 : Advanced Video Coding for Generic
Audiovisual Services", 2019,
<https://www.itu.int/rec/T-REC-H.264-201906-I/en>.
[Kim2001] Kim, T., "Scalable video Streaming Over Internet", Ph.D.
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[Kua2017] Kua, J., Armitage, G., and P. Branch, "A Survey of Rate
Adaptation Techniques for Dynamic Adaptive Streaming Over
HTTP", IEEE Communications Surveys and Tutorials, vol. 19,
no. 3, pp. 1842-1866, 2017,
<https://ieeexplore.ieee.org/document/7884970>.
[McCanne1996]
McCanne, S., Jacobson, V., and M. Vetterli, "Receiver-
Driven Layered Multicast", ACM Sigcomm, pp. 117-130,
1996,
<http://www.cs.toronto.edu/syslab/courses/csc2209/06au/
papers/recmc.pdf>.
[MPEG-DASH]
ISO/IEC, "23009-1:2019, Dynamic Adaptive Streaming over
HTTP (DASH) - Part 1: Media Presentation Description and
Segment Formats", 2019,
<https://www.iso.org/standard/79329.html>.
[MPEG-DASH-SAND]
ISO/IEC, "23009-5:2017, Dynamic Adaptive Streaming over
HTTP (DASH) - Part 5: Server and Network Assisted DASH
(SAND)", February 2017,
<https://www.iso.org/standard/69079.html>.
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[Orosz2015]
Orosz, P., Skopko, T., and P. Varga, "Towards Estimating
Video QoE Based on Frame Loss Statistics of the Video
Streams", DOI: 10.1109/INM.2015.7140482, IFIP/IEEE
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[RFC2475] Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.,
and W. Weiss, "An Architecture for Differentiated
Services", RFC 2475, December 1998,
<https://datatracker.ietf.org/doc/html/rfc2475>.
[RFC8836] Jesup, R. and Z. Sarker, "Congestion Control Requirements
for Interactive Real-Time Media", RFC 8836, January 2001,
<https://datatracker.ietf.org/doc/html/rfc8836>.
[Saadi2019]
Al-Saadi, R., Armitage, G., But, J., and P. Branch, "A
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Authors' Addresses
Lijun Dong
Futurewei Technologies Inc.
U.S.A
Email: [email protected]
Kiran Makhijani
Futurewei Technologies Inc.
U.S.A
Email: [email protected]
Richard Li
Futurewei Technologies Inc.
U.S.A
Email: [email protected]
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